Module 10 Assignment

Here is the code used to conduct ANOVA and regression analysis on the "cystfibr" and "secher" datasets in R Studio:

# Load necessary libraries

library(datasets)

# Load the cystfibr dataset

data("cystfibr")

# Fit a linear model to the data

model_cystfibr <- lm(spemax ~ age + weight + bmp + fev1, data=cystfibr)

# Display the summary of the model

summary(model_cystfibr)

# Conduct ANOVA on the model

anova(model_cystfibr)

# Load the secher dataset

data("secher")

# Log-transform birth weight and abdominal diameter

secher$log_bwt <- log(secher$bwt)

secher$log_ad <- log(secher$ad)

# Fit a linear model to the data

model_secher <- lm(log_bwt ~ log_ad, data=secher)

# Display the summary of the model

summary(model_secher)

In the cystfibr dataset, we’re fitting a linear model where spemax is predicted by age, weight, bmp, and fev1. The coefficients of these variables in the model represent their respective effects on spemax. The intercept is the expected value of spemax when all predictors are zero.

In the secher dataset, we’re fitting a linear model where log-transformed birth weight (log_bwt) is predicted by log-transformed abdominal diameter (log_ad). The coefficient of log_ad represents its effect on log_bwt. The intercept is the expected value of log_bwt when log_ad is zero.

The ANOVA table provides a statistical test of whether or not the means of several groups are equal. It does this by comparing the variance between groups to the variance within groups.